Skip Headers
Oracle® R Enterprise User's Guide
Release 1.4

E39886-01
Go to Documentation Home
Home
Go to Book List
Book List
Go to Table of Contents
Contents
Go to Index
Index
Go to Feedback page
Contact Us

Go to previous page
Previous
Go to next page
Next
PDF · Mobi · ePub

Changes in This Release for Oracle R Enterprise

Releases of Oracle R Enterprise often contain new features. The features in the current release and in some previous releases are described in the following topics:

Changes in Oracle R Enterprise 1.4

The following topics describe the changes in Oracle R Enterprise 1.4:

New Features in Oracle R Enterprise 1.4

The following changes are in Oracle R Enterprise 1.4:

  • Additions and improvements to data preparation functions:

    • The new factanal function performs factor analysis on a formula or an ore.frame object that contains numeric columns.

    • Both signatures of the princomp function support the scores, subset, and na.action arguments.

    • The new getXlevels function creates a list of factor levels that can be used in the xlev argument of a model.matrix call that involves an ore.frame object.

  • The new exploratory data analysis function ore.esm builds exponential smoothing models for time series data. The function builds a model using either the simple exponential smoothing method or the double exponential smoothing method. The function can preprocess the time series data with operations such as aggregation and the handling of missing values. See "Building Exponential Smoothing Models on Time Series Data".

  • Additions and improvements to the Oracle R Enterprise regression and neural network modeling functions:

    • The new ore.glm function provides methods for fitting generalized linear models, which include logistic regression, probit regression, and poisson regression. See "Building a Generalized Linear Model".

    • The ore.lm and ore.stepwise functions are no longer limited to a total of 1,000 columns when deriving columns in the model formula.

    • The ore.lm function now supports a weights argument for performing weighted least squares regression.

    • The anova function can now perform analysis of variance on an ore.lm object.

    • For the ore.stepwise function, the values for the direction argument have changed. The value "both" now prefers drops over adds. The new direction argument value "alternate" has the previous meaning of the "both" value.

    • The ore.neural function has several new arguments.

  • Additions and improvements to the Oracle Data Mining model algorithm functions:

  • An additional global option for Oracle R Enterprise, ore.parallel. See "Oracle R Enterprise Global Options".

Changes in Oracle R Enterprise 1.3

The following topics describe the changes in Oracle R Enterprise 1.3:

New Features in Oracle R Enterprise 1.3

The new features in Oracle R Enterprise 1.3 are the following:

  • Predicting with R models using in-database data with the OREpredict package

  • Ordering and indexing with row.names<-

  • Predicting with Oracle Data Mining models using the OREodm package

  • Saving and managing R objects in the database

  • Date and time data types

  • Sampling and partitioning

  • Long names for columns

  • Automatically connecting to an Oracle Database instance in embedded R scripts

  • Building an R neural network using in-database data with the ore.neural function

Other Changes in Oracle R Enterprise 1.3

Other changes in this release are the following:

Changes in Oracle R Enterprise 1.1

The new features in Oracle R Enterprise 1.1 are the following:

  • Support for additional operation systems:

    • Oracle R Distribution and Oracle R Enterprise are now supported IBM AIX 5.3 and higher and on 10 and higher for both 64-bit SPARC and 64-bit x386 (Intel) processors.

    • The Oracle R Enterprise Server now runs on 64-bit and 32-bit Windows operating systems.

  • Improved mathematics libraries in R:

    • You can now use the improved Oracle R Distribution with support for dynamically picking up either the Intel Math Kernel Library (MKL) or the AMD Core Math Library (ACML) with Oracle R Enterprise.

    • On Solaris, Oracle R Distribution dynamically links with Oracle SUN performance library for high speed BLAS and LAPACK operations.

  • Support for Oracle Wallet enables R scripts to no longer need to have database authentication credentials in clear text. Oracle R Enterprise is integrated with Oracle Wallet for that purpose.

  • Improved installation scripts provide more prerequisite checks and detailed error messages. Error messages provide specific instructions on remedial actions.